Identification of growth-coupled production strains considering protein costs and kinetic variability

Hoang V. Dinh, Zachary A. King, Bernhard O. Palsson, Adam M. Feist*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Conversion of renewable biomass to useful molecules in microbial cell factories can be approached in a rational and systematic manner using constraint-based reconstruction and analysis. Filtering for high confidence in silico designs is critical because in vivo construction and testing of strains is expensive and time consuming. As such, a workflow was devised to analyze the robustness of growth-coupled production when considering the biosynthetic costs of the proteome and variability in enzyme kinetic parameters using a genome-scale model of metabolism and gene expression (ME-model). A collection of 2632 unfiltered knockout designs in Escherichia coli was evaluated by the workflow. A ME-model was used in the workflow to test the designs’ growth-coupled production in addition to a less complex genome-scale metabolic model (M-model). The workflow identified 634 M-model growth-coupled designs which met the filtering criteria and 42 robust designs, which met growth-coupled production criteria using both M and ME-models. Knockouts were found to follow a pattern of controlling intermediate metabolite consumption such as pyruvate consumption and high flux subsystems such as glycolysis. Kinetic parameter sampling using the ME-model revealed how enzyme efficiency and pathway tradeoffs can affect growth-coupled production phenotypes.
Original languageEnglish
Article numbere00080
JournalMetabolic Engineering Communications
Volume7
ISSN2214-0301
DOIs
Publication statusPublished - 2018

Keywords

  • Endocrinology, Diabetes and Metabolism
  • Biomedical Engineering
  • alcohol
  • glucose
  • algorithm
  • Article
  • biomass production
  • cell growth
  • computer model
  • fermentation
  • gene expression
  • metabolism
  • nonhuman
  • oxygen consumption
  • phenotype
  • priority journal
  • reproducibility
  • Biotechnology
  • TP248.13-248.65
  • Biology (General)
  • QH301-705.5

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